Generalized exponential stability of neutral stochastic quaternion-valued neural networks with variable coefficients and infinite delay

Abstract

This paper focuses on neutral stochastic quaternion-valued neural networks (NSQNNs). Employing some stochastic analysis and inequalities techniques, we establish several sufficient conditions for ensuring $p$th moment generalized exponential stability. Our results do not require the construction of any Lyapunov function or rely on the assumption of bounded variable coefficients. Furthermore, our results expand some existing works. At last, to illustrate the efficacy of our result, we present one simulation example.